Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning | Paper Citation Record

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This paper cites Recent advances and applications of deep learning methods in materials science.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Recent advances and applications of deep learning methods in materials science

Reference 1

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This paper cites Evolution of artificial intelligence for application in contemporary materials science.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Evolution of artificial intelligence for application in contemporary materials science

Reference 2

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This paper cites Big data and machine learning for materials science.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Big data and machine learning for materials science

Reference 3

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This paper cites Deep learning in two -dimensional materials: Characterization, prediction, and design.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Deep learning in two -dimensional materials: Characterization, prediction, and design

Reference 4

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This paper cites A review of multi -scale and multi -physics simulations of metal additive manufacturing processes with focus on modeling strategies.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A review of multi -scale and multi -physics simulations of metal additive manufacturing processes with focus on modeling strategies

Reference 5

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Observation 1e220086-69ba-4357-9644-1d87e252c682 · outbound

This paper cites Materials fatigue prediction using graph neural networks on microstructure representations.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Materials fatigue prediction using graph neural networks on microstructure representations

Reference 6

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Observation 1edacde6-4e18-4f25-b741-75b4f5720015 · outbound

This paper cites Multi - physics approach to predict fatigue behavior of high strength aluminum alloy repaired via additive friction stir deposition.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Multi - physics approach to predict fatigue behavior of high strength aluminum alloy repaired via additive friction stir deposition

Reference 7

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Observation 94ba72dd-2cf7-4f95-b1c6-4d2d9f071369 · outbound

This paper cites A review on fatigue life prediction methods for metals.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A review on fatigue life prediction methods for metals

Reference 8

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Observation 56baa4b4-2db7-44e3-939a-1545ef289410 · outbound

This paper cites On the use of data -driven machine learning for probabilistic fatigue life prediction of metallic materials based on mesoscopic defect analysis.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning On the use of data -driven machine learning for probabilistic fatigue life prediction of metallic materials based on mesoscopic defect analysis

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:06.005119Z

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Observation 04b3a715-0cab-4b2e-84ce-26266335b116 · outbound

This paper cites Data -driven, physics-based, or both: Fatigue prediction of structural adhesive joints by artificial intelligence.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Data -driven, physics-based, or both: Fatigue prediction of structural adhesive joints by artificial intelligence

Reference 10

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Observation 128cf53c-fecb-4e44-9a73-c324f227fa36 · outbound

This paper cites Standard Practices for Cycle Counting in Fatigue Analysis.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Standard Practices for Cycle Counting in Fatigue Analysis

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.982923Z

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Observation 0f27d383-eb70-4167-b11a-46acfc8b5327 · outbound

This paper cites PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

Reference 12

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This paper cites Physics -informed machine learning.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Physics -informed machine learning

Reference 13

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This paper cites Physics -informed machine learning for modeling and control of dynamical systems.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Physics -informed machine learning for modeling and control of dynamical systems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.840434Z

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This paper cites Physics -informed machine learning for metal additive manufacturing.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Physics -informed machine learning for metal additive manufacturing

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.828560Z

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Observation af528355-d838-4ca4-87b3-c6b3b1223852 · outbound

This paper cites Embedding material graphs using the electron-ion potential: application to material fracture.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Embedding material graphs using the electron-ion potential: application to material fracture

Reference 16

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This paper cites A review of uncertainty quantification in deep learning: Techniques, applications and challenges.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.817719Z

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Observation 60a75e98-c26a-45c5-86f3-037f2ed632d3 · outbound

This paper cites Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks, Scientific Reports.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks, Scientific Reports

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.806945Z

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Observation 6d2ce467-0e57-48e4-a813-3cf1bb797822 · outbound

This paper cites A physics -informed neural network for creep -fatigue life prediction of components at elevated temperatures.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A physics -informed neural network for creep -fatigue life prediction of components at elevated temperatures

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.795965Z

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This paper cites Machine learning for metal additive manufacturing: Towards a physics -informed data -driven paradigm.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Machine learning for metal additive manufacturing: Towards a physics -informed data -driven paradigm

Reference 20

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This paper cites Machine learning -based fatigue life prediction of metal materials: Perspectives of physics -informed and data -driven hybrid methods.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Machine learning -based fatigue life prediction of metal materials: Perspectives of physics -informed and data -driven hybrid methods

Reference 21

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Observation 2848c57e-4182-4f61-a652-e223cfdc6f3c · outbound

This paper cites an unresolved cited work.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Unresolved cited work

Reference 22

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Observation dc47ce81-ac2d-4e9a-a535-4be8e5772501 · outbound

This paper cites MFLP -PINN: A physics -informed neural network for multiaxial fatigue life prediction.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning MFLP -PINN: A physics -informed neural network for multiaxial fatigue life prediction

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.751694Z

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This paper cites A novel fatigue and creep -fatigue life prediction model by combining data -driven approach with domain knowledge.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A novel fatigue and creep -fatigue life prediction model by combining data -driven approach with domain knowledge

Reference 24

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Observation d361fbb2-b018-476b-a5fb-cbbce7e36fd9 · outbound

This paper cites Mechanisms of fatigue crack initiation and growth.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Mechanisms of fatigue crack initiation and growth

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.729301Z

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Observation 7e9a1000-29e6-4185-899b-7894d36f8604 · outbound

This paper cites Crack propagation detection method in the structural fatigue process.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Crack propagation detection method in the structural fatigue process

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.718124Z

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Observation fbcf081d-30f3-4ea1-abb9-3b5822a0d00b · outbound

This paper cites A criterion for high -cycle fatigue life and fatigue limit prediction in biaxial loading conditions.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A criterion for high -cycle fatigue life and fatigue limit prediction in biaxial loading conditions

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.707152Z

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This paper cites Experimental and theoretical investigation of the frequency effect on low cycle fatigue of shape memory alloys.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Experimental and theoretical investigation of the frequency effect on low cycle fatigue of shape memory alloys

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.695768Z

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Observation 37fce4ad-b49b-4675-a1e1-f40cd2ba0bef · outbound

This paper cites Stress -life (S -N) Approach.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Stress -life (S -N) Approach

Reference 29

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This paper cites Essential structure of SN curve: Prediction of fatigue life and fatigue limit of defective materials and nature of scatter.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Essential structure of SN curve: Prediction of fatigue life and fatigue limit of defective materials and nature of scatter

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.670401Z

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Observation 85300045-e74b-4cd3-a97a-c81cedb26442 · outbound

This paper cites The determination of fatigue limits under alternating stress conditions.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning The determination of fatigue limits under alternating stress conditions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.660009Z

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Observation f11ceff9-bad8-4dd9-a7ba-525ab322cc05 · outbound

This paper cites The effect of stress ratio during crack propagation and fatigue for 2024 -T3 and 7075- T6 aluminum.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning The effect of stress ratio during crack propagation and fatigue for 2024 -T3 and 7075- T6 aluminum

Reference 32

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Observation 272bc118-8d7b-4214-af98-890fd8d1db4a · outbound

This paper cites 4-Stress and reliability analysis for interconnects.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning 4-Stress and reliability analysis for interconnects

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.638357Z

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Observation d753012e-de55-467f-a04e-64dd3b600d61 · outbound

This paper cites Modified Coffin-Manson equation to predict the fatigue life of structural materials subjected to mechanical -thermal coupling non -coaxial loading.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Modified Coffin-Manson equation to predict the fatigue life of structural materials subjected to mechanical -thermal coupling non -coaxial loading

Reference 34

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Observation 25beaa21-5edc-4904-a485-d5dabaac0900 · outbound

This paper cites Temperature Cycling Testing: Coffin -Manson Equation.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Temperature Cycling Testing: Coffin -Manson Equation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.615316Z

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Observation 635311cd-ead1-4b45-a820-00883e335eb8 · outbound

This paper cites Fatigue Crack Growth.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Fatigue Crack Growth

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.604526Z

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Observation 9358cd7d-dc7d-4307-adf1-f12c93b70f8e · outbound

This paper cites an unresolved cited work.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Unresolved cited work

Reference 37

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Observation 8081e65e-c984-4fa9-8de3-0844f98ff6b6 · outbound

This paper cites A stress-strain function for the fatigue of metals.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A stress-strain function for the fatigue of metals

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.580781Z

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Observation 4e2ab9f3-b087-4c52-bd68-52d4c93ad8c0 · outbound

This paper cites A critical plane approach to multiaxial fatigue damage including out of phase loading.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A critical plane approach to multiaxial fatigue damage including out of phase loading

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.568997Z

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Observation 10dad861-6846-45f3-b468-14fbef45f611 · outbound

This paper cites Equivalent energy-based critical plane fatigue damage parameter for multiaxial LCF under variable amplitude loading.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Equivalent energy-based critical plane fatigue damage parameter for multiaxial LCF under variable amplitude loading

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.558594Z

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Observation 215d3ec7-1bb7-4e04-a8a7-3dabb8295523 · outbound

This paper cites Quantile regression.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Quantile regression

Reference 41

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Observation 0f5c54a7-a388-47e5-9095-7a33b3b01443 · outbound

This paper cites Ngboost: Natural gradient boosting for probabilistic prediction.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Ngboost: Natural gradient boosting for probabilistic prediction

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.537287Z

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Observation 87875fb0-65b5-450c-bc3a-cee6f37eab28 · outbound

This paper cites A comparative analysis of gradient boosting algorithms.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A comparative analysis of gradient boosting algorithms

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.526741Z

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Observation 9077dd4b-060a-4901-8f43-7913f05a47ec · outbound

This paper cites Gaussian processes for regression.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Gaussian processes for regression

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.516507Z

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This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.505513Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a7f2f2b3-fe47-44f9-93b0-bb6a0af5a041 · outbound

This paper cites Dropout as a Bayesian approximation: Representing model uncertainty in deep learning.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Dropout as a Bayesian approximation: Representing model uncertainty in deep learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.494248Z

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Observation 9e046b11-7447-4495-b6ed-602c2cac59f6 · outbound

This paper cites Practical variational inference for neural networks.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Practical variational inference for neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.482645Z

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Observation bafc9cb6-5a33-4079-a9eb-e3d1da4344e7 · outbound

This paper cites Bayesian neural networks for uncertainty quantification in data-driven materials modelling.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Bayesian neural networks for uncertainty quantification in data-driven materials modelling

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.470196Z

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Observation c58fbc9f-c03f-4204-b575-efc12b8745dd · outbound

This paper cites Bayesian learning for neural networks.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Bayesian learning for neural networks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.459079Z

Source-reported events for the cited work

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Observation b94a2448-dba8-48ff-9f99-64839ff254d5 · outbound

This paper cites A Conceptual Introduction to Markov Chain Monte Carlo Methods.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A Conceptual Introduction to Markov Chain Monte Carlo Methods

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T14:43:05.309635Z

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Observation 5ff71c4a-526c-461f-8372-c78831a2132b · outbound

This paper cites Challenges in Markov chain Monte Carlo for Bayesian neural networks.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Challenges in Markov chain Monte Carlo for Bayesian neural networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.446721Z

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Observation 312eead1-5c77-4e0d-8dc4-d33250097a0c · outbound

This paper cites A deep learning-based life prediction method for components under creep, fatigue and creep-fatigue conditions.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning A deep learning-based life prediction method for components under creep, fatigue and creep-fatigue conditions

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.434399Z

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Observation 2ddfc3a0-f97c-4866-9b1c-9b75f2b460f4 · outbound

This paper cites Machine learning assisted interpretation of creep and fatigue life in titanium alloys.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Machine learning assisted interpretation of creep and fatigue life in titanium alloys

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.422541Z

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Observation 05a7a9d5-1e8e-4cb6-8aa0-58175d880e08 · outbound

This paper cites Mits.nims.go.jp (Accessed March 16, 2024).

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Mits.nims.go.jp (Accessed March 16, 2024)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.409343Z

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Observation fdd84fe3-5de9-42db-b5f8-c52951c00558 · outbound

This paper cites Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels.

Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:43:05.396410Z

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